CN106056234A - Transformer capacity determination method and device - Google Patents
Transformer capacity determination method and device Download PDFInfo
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- CN106056234A CN106056234A CN201610330959.0A CN201610330959A CN106056234A CN 106056234 A CN106056234 A CN 106056234A CN 201610330959 A CN201610330959 A CN 201610330959A CN 106056234 A CN106056234 A CN 106056234A
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a transformer capacity determination method and device. One specific embodiment of the method comprises the steps of: obtaining electricity load data of each commercial user; determining the transformer power distribution capacity index of each commercial user; determining a typical electricity load curve in a preset time interval of each commercial user, according to the proportion of the maximum value of the typical electricity load curve to the transformer power distribution capacity index, carrying out hour-by-hour load conversion on the typical electricity load curve, and generating a first characteristic value curve set; carrying out peak load shifting merging on the first characteristic value curve set, and generating a second characteristic value curve; and according to the maximum value of the second characteristic value curve, determining a total commercial transformer power distribution capacity. According to the embodiment, the capacity optimization of the power distribution transformer is realized, the capacity of the transformer is reduced, and resources are saved.
Description
Technical field
The application relates to building and supplies distribution technique field, is specifically related to distribution transformer technical field, particularly relates to transformation
The technical field of device capacity configuration.
Background technology
Along with the progress of society, the demand of electricity is continuously increased by each industry situation user, and the demand according to user is reasonable
Select distribution transformer capacity become an important problem.If the Capacity Selection of distribution transformer is excessive, transformation can be increased
The first current cost that device is implemented, actual load rate is low simultaneously, causes the wasting of resources；If the Capacity Selection of distribution transformer is too small, then
Actual loading can be caused excessive, even overload, it is difficult to meet the need for electricity of user, affect load stability.
User power utilization is in the end of distribution transformer, traditional distribution transformer design, is to hold from the end of transformator
Amount design starts, and first determines the distribution capacity index of transformator end, further according to end capacity, is multiplied by service demand factor, divided by negative
Lotus rate, divided by power factor, so that it is determined that end matched transformer capacitance.
But, existing matched transformer capacitance index relies on empirical value, and in order to meet the demand of user, typically results in
The capacity of design of transformer is much larger than the power consumption in reality operation；Meanwhile, distribution capacity index value is single, it is impossible to according to not
Same user chooses different desired values, and operating load rate is difficult to improve, and runs uneconomical.
Summary of the invention
The purpose of the application is to propose the determination method and apparatus of the transformer capacity of a kind of improvement, solves above back of the body
The technical problem that scape technology segment is mentioned.
First aspect, this application provides a kind of determination method of transformer capacity, and described method includes: obtain each industry situation
The power load data of user, wherein, described power load data include described each industry situation user within a predetermined period of time by
Time power load data；Determine the matched transformer capacitance index of described each industry situation user, wherein, described matched transformer capacitance
Index is generated after statistical analysis calculates by described power load data；Determine each industry situation typical case within a predetermined period of time
Power load curve, closes with the ratio of described matched transformer capacitance index according to the described maximum of typical case's power load curve
System, to described typical case power load curve carry out by time power load conversion, generate the First Eigenvalue collection of curves；To described
One eigenvalue graph set is avoided the peak hour merging, generates Second Eigenvalue curve；Using the maximum of described Second Eigenvalue curve as
Total industry situation matched transformer capacitance index, based on described total industry situation matched transformer capacitance index, determines total industry situation matched transformer
Capacitance.
In certain embodiments, the described matched transformer capacitance index determining described each industry situation user, comprise determining that institute
State meansigma methods and the variance of power load data；Based on described meansigma methods and variance, determine the transformator distribution of each industry situation user
Capacity performance index.
In certain embodiments, the described meansigma methods determining described power load data and variance, including: extract described use
The maximum of electric load data, generate by time power load data maximums set；By described by time power load data maximum
Value set is divided into n subset by different industry situations；Determine that described n son concentrates arithmetic mean of instantaneous value and the variance of each subset；Its
In, n is industry situation number, and n is more than or equal to 1.
In certain embodiments, described based on described meansigma methods and variance, determine the matched transformer electric capacity of each industry situation user
Figureofmerit, comprises determining that described n son concentrates the sample size value of each subset；Hold with described sample according to described meansigma methods
Value determines population sample average and the population sample variance of each subset, as following formula 1.-2. shown in:
Ai *=Ni·Ai①；
Wherein,It is the population sample average of the i-th subset,It is the population sample variance of the i-th subset, NiIt it is the i-th subset
Sample size, AiIt is the meansigma methods of the i-th subset, σiBeing the variance of the i-th subset, i is natural number and 1≤i≤n；Determine described side
Difference and the relation of described meansigma methods, 3. state with following formula:
σi=β Ai③；
Wherein, β is the variances sigma of described i-th subsetiMeansigma methods A with described i-th subsetiProportionality coefficient；Based on described
The population sample average of each subset and population sample variance, determine the matched transformer capacitance index of described each subset, as
Following formula is 4. shown:
Wherein, SiBeing the matched transformer capacitance index of the i-th subset, α is matched transformer capacitance index and population sample
The proportionality coefficient of average, 5. α can state with following formula:
In certain embodiments, the described typical power load curve determining each industry situation within a predetermined period of time, including:
Obtain described each industry situation user within a predetermined period of time by time power load curve；By described by time power load curve divide
For described n subset, generate described n subset by time power load group of curves；To described by time power load group of curves by
Time averaged, generate n bar typical case's power load curve, described n bar typical case's power load curve is each industry situation predetermined
Typical power load curve in time period.
In certain embodiments, described using the maximum of described Second Eigenvalue curve as total industry situation matched transformer electric capacity
Figureofmerit, based on described total industry situation matched transformer capacitance index, determines total industry situation matched transformer capacitance, including: according to public affairs
6. formula, determines total industry situation matched transformer capacitance, and formula is the most as described below:
Total industry situation matched transformer capacitance=total industry situation matched transformer capacitance index/rate of load condensate/power factor is 6., described
Total industry situation matched transformer capacitance index is the maximum of described Second Eigenvalue curve.
In certain embodiments, the power load data of described acquisition each industry situation user, including: according to standard deviation confidence district
Between with predetermined threshold value, described power load data are screened, filter out abnormal data.
Second aspect, this application provides the determination device of a kind of transformer capacity, and described device includes: data acquisition list
Unit, is configured to obtain the power load data of each industry situation user, and wherein, described power load data include that described each industry situation is used
Family within a predetermined period of time by time power load data；Matched transformer capacitance index unit, be configured to determine described respectively
The matched transformer capacitance index of industry situation user, wherein, described matched transformer capacitance index is by described power load data warp
Cross after statistical analysis calculates and generate；The First Eigenvalue collection of curves unit, is configured to determine that each industry situation is at predetermined amount of time
Interior typical power load curve, according to maximum and the described matched transformer capacitance index of described typical case's power load curve
Proportionate relationship, to described typical case power load curve carry out by time power load conversion, generate the First Eigenvalue collection of curves；
Second Eigenvalue curved unit, be configured to avoid the peak hour described the First Eigenvalue collection of curves merging, generates Second Eigenvalue bent
Line；Transformer capacity determines unit, is configured to the maximum of described Second Eigenvalue curve as total industry situation matched transformer
Capacitance index, based on described total industry situation matched transformer capacitance index, determines total industry situation matched transformer capacitance.
In certain embodiments, described matched transformer capacitance index unit includes: mean value calculation subelement, and configuration is used
In the meansigma methods and the variance that determine described power load data；Matched transformer capacitance index computation subunit, is configured to base
In described meansigma methods and variance, determine the matched transformer capacitance index of each industry situation user.
In certain embodiments, described mean value calculation subelement, including: data extraction module, it is configured to extract institute
State the maximum of power load data, generate by time power load data maximums set；Data allocation module, be configured to by
Described by time power load data maximums set be divided into n subset by different industry situations；Data computation module, is configured to really
Fixed described n son concentrates arithmetic mean of instantaneous value and the variance of each subset；Wherein, n is industry situation number, and n is more than or equal to 1.
In certain embodiments, described matched transformer capacitance index computation subunit includes: sample size computing module,
It is configured to determine the sample size value that described n son concentrates each subset；Population sample computing module, is configured to according to institute
State meansigma methods and described sample size value and determine population sample average and the population sample variance of each subset, as following formula 1.-2.
Shown in:
Ai *=Ni·Ai①；
Wherein,It is the population sample average of the i-th subset,It is the population sample variance of the i-th subset, NiIt it is the i-th subset
Sample size, AiIt is the meansigma methods of the i-th subset, σiBeing the variance of the i-th subset, i is natural number and 1≤i≤n；Coefficient calculations mould
Block, is configured to the relation determining described variance with described meansigma methods, 3. states with following formula:
σi=β Ai③；
Wherein, β is the variances sigma of described i-th subsetiMeansigma methods A with described i-th subsetiProportionality coefficient；Distribution capacity
Index computing module, is configured to the population sample average according to described each subset and population sample variance, determine described often
The matched transformer capacitance index of one subset, as following formula 4. shown in:
Wherein, SiBeing the matched transformer capacitance index of the i-th subset, α is transformator distribution safety coefficient, and α can use following formula
5. statement:
In certain embodiments, described the First Eigenvalue collection of curves unit, including: curve acquisition module, it is configured to
Obtain described each industry situation user within a predetermined period of time by time power load curve；Curve distribution module, is configured to institute
State by time power load curve be divided into described n subset, generate described n subset by time power load group of curves；Curve
Computing module, be configured to described by time power load group of curves by time averaged, generate n bar typical case's power load bent
Line, described n bar typical case's power load curve is each industry situation typical power load curve within a predetermined period of time.
In certain embodiments, described depressor capacity determines that cell location is further used for: according to formula 6., determines total industry
State matched transformer capacitance, formula is the most as described below:
Total industry situation matched transformer capacitance=total industry situation matched transformer capacitance index/rate of load condensate/power factor is 6., described
Total industry situation matched transformer capacitance index is the maximum of described Second Eigenvalue curve.
In certain embodiments, described data acquisition unit includes: data screening module, is configured to put according to standard deviation
Described power load data are screened by letter interval with predetermined threshold value, filter out abnormal data.
The determination method and apparatus of the transformer capacity that the application provides, by data acquisition accurately and computational analysis,
Obtain the matched transformer capacitance index of each industry situation, and determined the eigenvalue graph of each industry situation by data analysis, based on each industry
The peak value difference of state eigenvalue graph carries out merging of avoiding the peak hour, and determines transformer capacity, sufficiently make use of the space of transformator, fall
The low distribution capacity of transformator, improves the operating load rate of transformator.
Accompanying drawing explanation
By the detailed description that non-limiting example is made made with reference to the following drawings of reading, other of the application
Feature, purpose and advantage will become more apparent upon:
Fig. 1 is that the application can apply to exemplary system architecture figure therein；
Fig. 2 is the flow chart of an embodiment of the determination method of the transformer capacity according to the application；
Fig. 3 is the curve chart of an application scenarios of the determination method of the transformer capacity according to the application；
Fig. 4 is the flow chart of another embodiment of the determination method of the transformer capacity according to the application；
Fig. 5 is the structural representation of an embodiment of the determination method of the transformer capacity according to the application.
Detailed description of the invention
With embodiment, the application is described in further detail below in conjunction with the accompanying drawings.It is understood that this place is retouched
The specific embodiment stated is used only for explaining related invention, rather than the restriction to this invention.It also should be noted that, in order to
It is easy to describe, accompanying drawing illustrate only the part relevant to about invention.
It should be noted that in the case of not conflicting, the embodiment in the application and the feature in embodiment can phases
Combination mutually.Describe the application below with reference to the accompanying drawings and in conjunction with the embodiments in detail.
Fig. 1 shows the determination method of the transformer capacity that can apply the application or the determination device of transformer capacity
The exemplary system architecture 100 of embodiment.
As it is shown in figure 1, system architecture 100 can include terminal unit 101,102,103, network 104,106, server
105 and entity device 107,108,109.Network 104,106 is in order between terminal unit 101,102,103 and server 105
Or the medium of communication link is provided between server 105 and entity device 107,108,109.Network 104 can include various
Connection type, the most wired, wireless communication link or fiber optic cables etc..
User can use terminal unit 101,102,103 mutual, to send electricity consumption with server 105 by network 104
The information such as user data information, running parameter.Can be provided with various telecommunication customer end on terminal unit 101,102,103 should
With, such as web browser applications, industrial control software etc. can be used for sending the software of instruction to server 105.
Terminal unit 101,102,103 can be the various electronic equipments with display screen, includes but not limited to intelligence hands
Machine, panel computer, E-book reader, MP3 player (Moving Picture Experts Group Audio Layer
III, dynamic image expert's compression standard audio frequency aspect 3), MP4 (Moving Picture Experts Group Audio
Layer IV, dynamic image expert's compression standard audio frequency aspect 4) player, pocket computer on knee and desk computer etc.
Deng.
Parameter corresponding for transformer capacity matching scheme can be exported terminal unit 101,102,103 by background server
On show.
The determination method for transformer capacity that the embodiment of the present application is provided typically is performed by server 105, accordingly
Ground, the determination device for transformer capacity is generally positioned in server 105.
With continued reference to Fig. 2, it is shown that according to the flow process of an embodiment of the determination method of the transformer capacity of the application
200.The determination method of described transformer capacity, comprises the following steps:
Step 201, obtains the power load data of each industry situation user.
In the present embodiment, electricity user can be divided into different kinds according to the difference of industry situation, substantially can be divided into retail
Class, food and drink class, amusement electronic game class, office (containing air-conditioning) class, apartment office class, public affairs district, market power category, market refrigeration plant (include
The air conditioning area in unit work area), the different classification in office refrigeration plant, garage etc., select from the electricity consumption user of these classifications respectively
Take the electricity consumption users such as representational user, the shopping centre on the most a certain square, Office Area, parking lot, obtain the electricity consumption of user
Load data is as sample, and the sample size that every kind of industry situation is chosen is not less than 10, and the sample data chosen includes: electricity consumption user
The power load data of every day in 1 year.
Step 202, determines the matched transformer capacitance index of each industry situation user.
In the present embodiment, based on above-mentioned steps 201, after obtaining the power load data of user, each industry situation has
The power load data sample of multiple same industry situation users, carries out computational analysis to the power load data sample of each industry situation,
Determining a matched transformer capacitance index corresponding for each industry situation, wherein matched transformer capacitance index is transformator
The index that total capacity obtains divided by its end load area, unit is VA/m2。
Step 203, determines each industry situation typical power load curve within a predetermined period of time, according to typical case's power load
The maximum of curve and the relation of matched transformer capacitance index, generate the First Eigenvalue collection of curves.
In the present embodiment, according to above-mentioned steps 201, after obtaining the power load data of different industry situation user, to difference
The power load data of industry situation user carry out statistical analysis, it may be determined that each industry situation have within certain period one based on
Maximum power load curve during this period of time, is defined as the typical power load curve of each industry situation by this load curve.
Having a maximum of points on above-mentioned typical case's power load curve, this maximum of points is that the peak value in above-mentioned certain period is born
Lotus.This typical case's power load curve include each industry situation peakload day by time power load curve.By this peak load
The matched transformer capacitance index that point determines according to above-mentioned steps 202 contrasts, according to the actual electricity consumption of each industry situation user
The demand of amount, determines the ratio value of above-mentioned peak load and above-mentioned matched transformer capacitance index.According to this ratio value, to above-mentioned
On the typical power load curve of each industry situation, the load value of each time point converts, obtain each industry situation based on ratio
New eigenvalue graph after value conversion.The new eigenvalue graph of each industry situation is placed in same set, generates first
Eigenvalue graph set.
In some optional implementations of the present embodiment, determine the typical electricity consumption within a predetermined period of time of each industry situation
Load curve can be carried out as follows: first, obtain each industry situation user within a predetermined period of time by time power load curve；Its
Secondary, will by time power load curve be n subset according to each industry situation different demarcation, generate n subset by time power load
Group of curves；Finally, to above-mentioned by time power load group of curves by time averaged, generate n bar typical case's power load curve, on
Stating n bar typical case's power load curve is each industry situation typical power load curve within a predetermined period of time.Wherein, n is industry situation
Number, and n is more than or equal to 1.
Step 204, merging that the First Eigenvalue collection of curves is avoided the peak hour, generate Second Eigenvalue curve.
In the present embodiment, according in the First Eigenvalue collection of curves that above-mentioned steps 203 determines, each eigenvalue song
The peak load point of line will not occur at synchronization, and the peak load point occurred according to the eigenvalue graph of each industry situation is not
With, each eigenvalue graph in the First Eigenvalue collection of curves is carried out, according to time relation one to one, conjunction of avoiding the peak hour
And, generate Second Eigenvalue curve.
Step 205, using the maximum of Second Eigenvalue curve as total industry situation matched transformer capacitance index, based on total industry
State matched transformer capacitance index, determines total industry situation matched transformer capacitance.
In the present embodiment, the Second Eigenvalue curve determined according to above-mentioned steps 204, choose Second Eigenvalue curve
Maximum of points, using this maximum of points as the matched transformer capacitance index of total industry situation, according to the matched transformer electric capacity of total industry situation
Figureofmerit determines the matched transformer capacitance of total industry situation.
In some optional implementations of the present embodiment, based on described total industry situation matched transformer capacitance index, really
Fixed total industry situation matched transformer capacitance, including: according to following formula:
Total industry situation matched transformer capacitance=total industry situation matched transformer capacitance fertilizer index/rate of load condensate/power factor, really
Determine matched transformer capacitance.
It it is a song of the application scenarios of the determination method of the transformer capacity according to the present embodiment with continued reference to Fig. 3, Fig. 3
Line chart.Schematic diagram gives 8 kinds of different industry situations intraday by time power load curve, wherein, label 1 for food and drink class by
Time power load curve, label 2 for amusement electronic game class by time power load curve, label 3 for retail class by time power load bent
Line, label 4 for office (containing air-conditioning) class by time power load curve, label 5 be market refrigeration plant (unit air conditioning area) by
Time power load curve；Label 6 be minimized office class by time power load curve, label 7 is office refrigeration plant by time electricity consumption
Load curve, label 8 be garage by time power load curve.
As a example by the representative value load curve of food and drink class, amusement electronic game class and retail class these three industry situation, first obtain
Food and drink class, amusement electronic game class and retail class these three industry situation under individual consumer in sometime by time power load data,
Determine food and drink class, amusement electronic game class and the matched transformer capacitance index of retail class respectively, simultaneously according to food and drink class, amusement electricity
Play class and retail class these three industry situation by time power load data draw each industry situation by time power load curve, according to
The representative value load curve of these three industry situation and matched transformer capacitance index, generate eigenvalue based on these three industry situation bent
The curve of these three industry situation in line, i.e. Fig. 3.From figure 3, it can be seen that each industry situation is when one day internal loading peak value occurs
Between different, based on this, merging that the eigenvalue graph of these three industry situation is avoided the peak hour, can obtain based on these three industry situation total
Eigenvalue graph, chooses the maximum of total characteristic value curve as the total matched transformer capacitance index of these three industry situation, according to
This matched transformer capacitance index, determines overall matched transformer capacitance based on these three industry situation.
With further reference to Fig. 4, it illustrates another embodiment of the determination method of the transformer capacity according to the application
Flow process 400.The flow process 400 of the determination method of this transformer capacity, comprises the following steps:
Step 401, obtains the power load data of each industry situation user.
Electricity user can be divided into different kinds according to the difference of industry situation, selects respectively from the electricity consumption user of these classifications
Taking representational user, obtain the power load data of user as sample, the sample size that every kind of industry situation is chosen is not less than
10, the sample data chosen includes: electricity consumption user is the power load data of every day in 1 year.
Power load data are screened, are filtered out abnormal data by step 402.
In the present embodiment, based on above-mentioned steps 401, real departing from user owing to data transmission procedure can produce some
The abnormal data of border range, such as data are excessive or too small, arrange thresholding threshold in the entity device in above-mentioned framework 100
Value, uses the method for standard deviation confidence interval to screen the power load data of user, filters out abnormal data.Meanwhile,
Data can be carried out repeatedly Cycle Screening, expand the scope of confidence interval, improve the accuracy of data.
Step 403, determines meansigma methods and the variance of power load data.
In the present embodiment, the data after step 402 being screened calculate, putting down of the power load data of acquisition user
Average and variance, can be carried out as follows: first, extracts the maximum of power load data, generate by time power load data
Big value set；Secondly, by by time power load data maximums set be divided into the n in above-mentioned steps 203 by different industry situations
Subset；Finally, determine that n son concentrates arithmetic mean of instantaneous value and the variance of each subset.
Step 404, based on meansigma methods and variance, determines the matched transformer capacitance index of each industry situation user.
In the present embodiment, based on step 403, by calculating meansigma methods and the side of variance of the power load data of user
Formula, determines the matched transformer capacitance index of each industry situation user, can be carried out as follows: determine n subset in above-mentioned steps 402
In the sample size value of each subset；
The population sample average and totally of each subset is determined according to the meansigma methods in step 402 and above-mentioned sample size value
Sample variance, as following formula 1.-2. shown in:
Ai *=Ni·Ai①；
Wherein,It is the population sample average of the i-th subset,It is the population sample variance of the i-th subset, NiIt it is the i-th subset
Sample size, AiIt is the meansigma methods of the i-th subset, σiBeing the variance of the i-th subset, i is natural number and 1≤i≤m；
Determine variance and the relation of meansigma methods in step 402,3. state with following formula:
σi=β Ai③；
Wherein, β is the variances sigma of above-mentioned i-th subsetiMeansigma methods A with above-mentioned i-th subsetiProportionality coefficient；
Population sample average based on each subset and population sample variance, determine the matched transformer capacitance of each subset
Index, as following formula 4. shown in:
Wherein, SiBeing the matched transformer capacitance index of the i-th subset, α is matched transformer capacitance index and population sample
The proportionality coefficient of average, 5. available following formula state:
Concrete, to the power load data of user after said method carries out quantitative analysis, obtain above-mentioned variance with
The value of the proportionality coefficient β of meansigma methods is 0.2～0.6；As a example by user's sample of same industry situation, in proportionality coefficient β value 0.6
In the case of, table one gives the matched transformer capacitance index under the Different Sample value of same industry situation.
Table one sample capability value and the relation of matched transformer capacitance index
By table one it can be seen that along with sample size is gradually increased, matched transformer capacitance index is closer to above-mentioned user
The sample mean of volume of distribution, in same industry situation, the sample size value of user is no less than 10.When sample size value 10
Time, the matched transformer capacitance index of same industry situation is the meansigma methods of these industry situation power load data of 1.2 times.
Step 405, determines each industry situation typical power load curve within a predetermined period of time, according to typical case's power load
The maximum of curve and the relation of matched transformer capacitance index, generate the First Eigenvalue collection of curves.
In the present embodiment, the step 203 in embodiment corresponding to step 401 and Fig. 2 is essentially identical, the most superfluous
State.
Step 406, merging that the First Eigenvalue collection of curves is avoided the peak hour, generate Second Eigenvalue curve.
In the present embodiment, the step 204 in embodiment corresponding to step 401 and Fig. 2 is essentially identical, the most superfluous
State.
Step 407, using the maximum of Second Eigenvalue curve as total industry situation matched transformer capacitance index, based on total industry
State matched transformer capacitance index, determines total industry situation matched transformer capacitance.
In the present embodiment, the step 205 in embodiment corresponding to step 401 and Fig. 2 is essentially identical, the most superfluous
State.
Figure 4, it is seen that unlike the embodiment corresponding from Fig. 2, the transformer capacity in the present embodiment is really
Determine the flow process 400 of method to have had more power load data are screened, filter out the step 402 of abnormal data, determine electricity consumption
The meansigma methods of load data and the step 403 of variance and based on meansigma methods and variance, determine the transformator distribution of each industry situation user
The step 404 of capacity performance index.By the step 402,403 and 404 that increase, the scheme that the present embodiment describes can more precisely really
Determine matched transformer capacitance index, enhance the accuracy of the power load data of user and matched transformer capacitance index
Reliability.
With further reference to Fig. 5, as to the realization of method shown in above-mentioned each figure, this application provides a kind of transformer capacity
An embodiment of determination device, this device embodiment is corresponding with the embodiment of the method shown in Fig. 2, and this device is the most permissible
It is applied in various electronic equipment.
As it is shown in figure 5, the determination device 500 of the transformer capacity described in the present embodiment includes: data acquisition unit 501,
Matched transformer capacitance index unit 502, the First Eigenvalue collection of curves unit 503, Second Eigenvalue curved unit 504 and change
Depressor capacity cell.Wherein, data acquisition unit 501 is configured to obtain the power load data of each industry situation user, wherein, uses
Electric load data include each industry situation user within a predetermined period of time by time power load data；Matched transformer capacitance index list
Unit 502 is configured to determine the matched transformer capacitance index of above-mentioned each industry situation user；Wherein, matched transformer capacitance index by
Above-mentioned power load data generate after being calculated analytically；The First Eigenvalue collection of curves unit 503 is configured to determine each
Industry situation typical power load curve within a predetermined period of time, according to maximum and the above-mentioned transformator of typical case's power load curve
The proportionate relationship of distribution capacity index, to typical case power load curve carry out by time power load conversion, generate the First Eigenvalue
Collection of curves；Second Eigenvalue curved unit 504 is configured to avoid the peak hour above-mentioned the First Eigenvalue collection of curves merging, generates the
Two eigenvalue graph；Transformer capacity unit 505 is configured to the maximum of described Second Eigenvalue curve as total industry situation
Matched transformer capacitance index, based on described total industry situation matched transformer capacitance index, determines total industry situation matched transformer capacitance.
In the present embodiment, the power load data 501 of the user of the determination device 500 of transformer capacity can be from this locality
Or remotely obtain the power load data message of user, specifically, when 500, the determination device of above-mentioned transformer capacity
Time on the background server that can directly obtain the power load data of user, the power load data 501 of user are permissible
Directly obtain above-mentioned power load data from server local；And when the determination device 500 of transformer capacity is positioned at remote terminal
Time on equipment, the power load data 501 of user can be by wired connection mode or radio connection from can be direct
The background server of the power load data message obtaining user obtains.Here, the power load data of user include each industry
State user within a predetermined period of time by time power load data.
In the present embodiment, after data acquisition unit 501 obtains the power load data of user each industry situation user, become
Depressor distribution capacity index unit 502 may be used for the power load data of above-mentioned each industry situation user are carried out computational analysis, really
The matched transformer capacitance index of fixed each industry situation user.
In the present embodiment, the First Eigenvalue collection of curves unit 503 of the determination device 500 of transformer capacity can be right
Power load data and each industry situation of matched transformer capacitance index unit 502 acquisition that data acquisition unit 501 obtains are used
The matched transformer point capacity performance index at family carries out Macro or mass analysis, generates the First Eigenvalue collection of curves unit.
In the present embodiment, the Second Eigenvalue curved unit 504 of the determination device 500 of transformer capacity can be above-mentioned
The First Eigenvalue collection of curves that the First Eigenvalue collection of curves unit 503 generates carries out merging of avoiding the peak hour, and generates one based on always
The eigenvalue graph of industry situation load, i.e. Second Eigenvalue curve.
In the present embodiment, the transformer capacity unit 505 of the determination device 500 of transformer capacity can be to above-mentioned second
The curve of eigenvalue graph unit 504 is analyzed, and determines the maximum of Second Eigenvalue curve, using this maximum as total industry
State matched transformer capacitance index, according to this total industry situation matched transformer capacitance index, determines the transformer capacity of total industry situation.
In an optional embodiment of the present embodiment, the data acquisition of the determination device 500 of above-mentioned transformer capacity
Unit 501 farther includes: data screening module (not shown), is configured to according to standard deviation confidence interval and predetermined threshold value pair
Described power load data are screened.The matched transformer capacitance index unit of the determination device 500 of above-mentioned transformer capacity
502 farther include: mean value calculation subelement (not shown), be configured to determine the meansigma methods of described power load data and
Variance；Matched transformer capacitance index computation subunit (not shown), is configured to based on described meansigma methods and variance, determines each
The matched transformer capacitance index of industry situation user.Mean value calculation subelement (not shown) farther includes: data extraction module
(not shown), is configured to extract the maximum of described power load data, generate by time power load data maximums set；
Data allocation module (not shown), be configured to by described by time power load data maximums set be divided into by different industry situations
N subset；Data computation module (not shown), be configured to determine described n son concentrate each subset arithmetic mean of instantaneous value and
Variance；Wherein, n is industry situation number, and n is more than or equal to 1.Matched transformer capacitance index computation subunit (not shown) is further
Including: sample size computing module (not shown), it is configured to determine the sample size value that described n son concentrates each subset；
Population sample computing module (not shown), is configured to determine each subset according to described meansigma methods and described sample size value
Population sample average and population sample variance；Coefficients calculation block (not shown), is configured to determine that described variance is flat with described
The relation of average；Distribution capacity index computing module, is configured to population sample average based on described each subset with overall
Sample variance, determines the matched transformer capacitance index of described each subset.The number of the determination device 500 of above-mentioned transformer capacity
Farther include according to collecting unit 503: the First Eigenvalue collection of curves unit (not shown), be configured to determine that each industry situation exists
Typical power load curve in predetermined amount of time, according to maximum and the described matched transformer of described typical case's power load curve
The proportionate relationship of capacitance index, to described typical case power load curve carry out by time power load conversion, generate fisrt feature
Value collection of curves.The First Eigenvalue collection of curves unit (not shown) farther includes: curve acquisition module (not shown), configuration
For obtain described each industry situation user within a predetermined period of time by time power load curve；Curve distribution module (not shown),
Be configured to by described by time power load curve be divided into described n subset, generate described n subset by time power load
Group of curves；Curve computing module (not shown), be configured to described by time power load group of curves by time averaged, raw
Becoming n bar typical case's power load curve, described n bar typical case's power load curve is that each industry situation typical case within a predetermined period of time uses
Electric load curve.
It will be understood by those skilled in the art that above-mentioned information push-delivery apparatus 500 also includes some other known features, such as
Processor, memorizer etc., embodiment of the disclosure in order to unnecessarily fuzzy, structure known to these is the most not shown.
Unit involved in the embodiment of the present application or module can realize by the way of software, it is also possible to by firmly
The mode of part realizes.Described unit or module can also be arranged within a processor, for example, it is possible to be described as: at Yi Zhong
Reason device includes data acquisition unit, matched transformer capacitance index unit, the First Eigenvalue collection of curves unit, Second Eigenvalue
Curved unit and transformer capacity unit.Wherein, the title of these unit is not intended that under certain conditions to this unit itself
Restriction, such as, data acquisition unit is also described as " being configured to obtain the power load data of each industry situation user
Unit ".
As on the other hand, present invention also provides a kind of computer-readable recording medium, this computer-readable storage medium
Matter can be the computer-readable recording medium described in above-described embodiment included in device；Can also be individualism, not
The computer-readable recording medium being fitted in terminal.Described computer-readable recording medium storage have one or more than one
Program, described program is used for performing to be described in the transformer capacity configuration side of the application by one or more than one processor
Method.
Claims (14)
1. the determination method of a transformer capacity, it is characterised in that described method includes:
Obtaining the power load data of each industry situation user, wherein, described power load data include that described each industry situation user is in advance
In the section of fixing time by time power load data；
Determining the matched transformer capacitance index of described each industry situation user, wherein, described matched transformer capacitance index is by described
Power load data generate after statistical analysis calculates；
Determine each industry situation typical power load curve within a predetermined period of time, according to described typical case's power load curve
The proportionate relationship of big value and described matched transformer capacitance index, described typical case's power load curve is carried out by time power load
Conversion, generates the First Eigenvalue collection of curves；
Described the First Eigenvalue collection of curves is avoided the peak hour merging, generate Second Eigenvalue curve；
Using the maximum of described Second Eigenvalue curve as total industry situation matched transformer capacitance index, become based on described total industry situation
Depressor distribution capacity index, determines total industry situation matched transformer capacitance.
Method the most according to claim 1, it is characterised in that the described matched transformer electric capacity determining described each industry situation user
Figureofmerit, including:
Determine meansigma methods and the variance of described power load data；
Based on described meansigma methods and variance, determine the matched transformer capacitance index of each industry situation user.
Method the most according to claim 2, it is characterised in that the described meansigma methods determining described power load data and side
Difference, including:
Extract the maximum of described power load data, generate by time power load data maximums set；
By described by time power load data maximums set be divided into n subset by different industry situations；
Determine that described n son concentrates arithmetic mean of instantaneous value and the variance of each subset；Wherein, n is industry situation number, and n is more than or equal to
1。
Method the most according to claim 3, it is characterised in that described based on described meansigma methods and variance, determines each industry situation
The matched transformer capacitance index of user, including:
Determine that described n son concentrates the sample size value of each subset；
Population sample average and the population sample variance of each subset is determined according to described meansigma methods and described sample size value, as
Following formula 1.-2. shown in:
Ai *=Ni·Ai①；
Wherein,It is the population sample average of the i-th subset,It is the population sample variance of the i-th subset, NiIt it is the sample of the i-th subset
This capacity, AiIt is the meansigma methods of the i-th subset, σiBeing the variance of the i-th subset, i is natural number and 1≤i≤n；
Determine the relation of described variance and described meansigma methods, 3. state with following formula:
σi=β Ai③；
Wherein, β is the variances sigma of described i-th subsetiMeansigma methods A with described i-th subsetiProportionality coefficient；
Population sample average based on described each subset and population sample variance, determine the transformator distribution of described each subset
Capacity performance index, as following formula 4. shown in:
Wherein, SiBeing the matched transformer capacitance index of the i-th subset, α is matched transformer capacitance index and population sample average
Proportionality coefficient, 5. α can state with following formula:
Method the most according to claim 3, it is characterised in that the described typical case's use determining each industry situation within a predetermined period of time
Electric load curve, including:
Obtain described each industry situation user within a predetermined period of time by time power load curve；
By described by time power load curve be divided into described n subset, generate described n subset by time power load curve
Group；
To described by time power load group of curves by time averaged, generate n bar typical case's power load curve, described n bar allusion quotation
Type power load curve is each industry situation typical power load curve within a predetermined period of time.
Method the most according to claim 1, it is characterised in that the described maximum using described Second Eigenvalue curve is as always
Industry situation matched transformer capacitance index, based on described total industry situation matched transformer capacitance index, determines total industry situation transformator distribution
Capacity, including:
According to formula 6., determining total industry situation matched transformer capacitance, formula is the most as described below:
Always industry situation matched transformer capacitance=total industry situation matched transformer capacitance index/rate of load condensate/power factor is 6., described total industry
State matched transformer capacitance index is the maximum of described Second Eigenvalue curve.
Method the most according to claim 1, it is characterised in that the power load data of described acquisition each industry situation user, bag
Include:
With predetermined threshold value, described power load data are screened according to standard deviation confidence interval, filter out abnormal data.
8. the determination device of a transformer capacity, it is characterised in that described device includes:
Data acquisition unit, is configured to obtain the power load data of each industry situation user, wherein, described power load packet
Include described each industry situation user within a predetermined period of time by time power load data；
Matched transformer capacitance index unit, is configured to determine the matched transformer capacitance index of described each industry situation user, its
In, described matched transformer capacitance index is generated after statistical analysis calculates by described power load data；
The First Eigenvalue collection of curves unit, is configured to determine that each industry situation typical power load within a predetermined period of time is bent
Line, according to maximum and the proportionate relationship of described matched transformer capacitance index of described typical case's power load curve, to described
Typical case power load curve carry out by time power load conversion, generate the First Eigenvalue collection of curves；
Second Eigenvalue curved unit, be configured to avoid the peak hour described the First Eigenvalue collection of curves merging, generates second feature
Value curve；
Transformer capacity unit, is configured to the maximum of described Second Eigenvalue curve as total industry situation matched transformer electric capacity
Figureofmerit, based on described total industry situation matched transformer capacitance index, determines total industry situation matched transformer capacitance.
Device the most according to claim 8, it is characterised in that described matched transformer capacitance index unit includes:
Mean value calculation subelement, is configured to determine meansigma methods and the variance of described power load data；
Matched transformer capacitance index computation subunit, is configured to based on described meansigma methods and variance, determines each industry situation user
Matched transformer capacitance index.
Device the most according to claim 9, it is characterised in that described mean value calculation subelement, including:
Data extraction module, is configured to extract the maximum of described power load data, generate by time power load data
Big value set；
Data allocation module, be configured to by described by time power load data maximums set be divided into n by different industry situations
Subset；
Data computation module, is configured to determine arithmetic mean of instantaneous value and the variance that described n son concentrates each subset；Wherein, n is
Industry situation number, and n is more than or equal to 1.
11. devices according to claim 10, it is characterised in that described matched transformer capacitance index computation subunit,
Including:
Sample size computing module, is configured to determine the sample size value that described n son concentrates each subset；
Population sample computing module, is configured to determine the overall of each subset according to described meansigma methods with described sample size value
Sample average and population sample variance, as following formula 1.-2. shown in:
Ai *=Ni·Ai①；
Wherein,It is the population sample average of the i-th subset,It is the population sample variance of the i-th subset, NiIt it is the sample of the i-th subset
This capacity, AiIt is the meansigma methods of the i-th subset, σiBeing the variance of the i-th subset, i is natural number and 1≤i≤n；
Coefficients calculation block, is configured to the relation determining described variance with described meansigma methods, 3. states with following formula:
σi=β Ai③；
Wherein, β is the variances sigma of described i-th subsetiMeansigma methods A with described i-th subsetiProportionality coefficient；
Distribution capacity index computing module, is configured to population sample average based on described each subset and population sample side
Difference, determine the matched transformer capacitance index of described each subset, as following formula 4. shown in:
Wherein, SiBeing the matched transformer capacitance index of the i-th subset, α is matched transformer electrostrictive coefficient, and 5. α can state with following formula:
12. devices according to claim 10, it is characterised in that described the First Eigenvalue collection of curves unit, including:
Curve acquisition module, be configured to obtain described each industry situation user within a predetermined period of time by time power load curve；
Curve distribution module, be configured to by described by time power load curve be divided into described n subset, generate described n individual
Subset by time power load group of curves；
Curve computing module, be configured to described by time power load group of curves by time averaged, generate n bar typical case use
Electric load curve, described n bar typical case's power load curve is each industry situation typical power load curve within a predetermined period of time.
13. devices according to claim 8, it is characterised in that described transformer capacity determines that cell location is used further
In:
According to formula 6., determining total industry situation matched transformer capacitance, formula is the most as described below: total industry situation matched transformer capacitance=
6., described total industry situation matched transformer capacitance index is described to total industry situation matched transformer capacitance index/rate of load condensate/power factor
The maximum of Second Eigenvalue curve.
14. devices according to claim 8, it is characterised in that described data acquisition unit includes:
Data screening module, is configured to sieve described power load data with predetermined threshold value according to standard deviation confidence interval
Choosing, filters out abnormal data.
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| CN106921158B (en) * | 2017-02-09 | 2020-01-31 | 国网福建省电力有限公司 | demand coefficient analysis method for historical collected data based on time sequence of distribution transformer |
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